O-447 Association of Perceived Job Security and Chronic Health Conditions with Retirement in Older UK and U.S Workers
Bibliographic record
Abstract
Background The relationship between job insecurity, chronic health conditions (CHCs), and retirement among older workers are likely to differ between countries that have different labor markets and health and social safety nets. To date, there are no epidemiological studies that have prospectively assessed the role of job insecurity in retirement incidence, while accounting for CHCs in two countries with vastly different welfare systems. We investigated the strength of the association baseline job insecurity and retirement incidence over an 11-year period while accounting for CHCs, among workers aged 50 and above in the UK and U.S. Methods We performed Cox proportional hazards regression analysis, using data from the Health and Retirement Study (HRS [U.S. cohort, n=491]) and English Longitudinal Study on Aging (ELSA [UK cohort n=821]). Results We found evidence of reduced likelihood of retirement among job insecure adults in both cohorts, and a significant association between CHCs and retirement in the U.S cohort only. In the UK cohort, the association between job insecurity and decreased retirement incidence (HR=0.69, 95% CI =0.50–0.95) was attenuated after adjustment for CHCs and covariates. In the U.S cohort, adjustment for CHCs and other social and health factors significantly decreased this association (HR=0.60, 95%CI = 0.36–0.99), indicating that CHCs, social, and health factors are contributing mechanistic factors underpinning retirement incidence in the U.S. Conclusions The country level differences we observed may be driven by macro level factors operating latently, which may affect the work environment, health outcomes, and retirement decisions uniquely in different settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".